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@article{182389,
        author = {RATNAM AKHIL and NELAKURTHI JASWANTH SRI HARSHA and CHILAKALAPUDI L V SIVA SAI RAMA KRISHNA and JAYA KRISHNA UPPULURI},
        title = {Pufferfish Optimization based Deep Convolutional Neural Network for Malware Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1691-1698},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182389},
        abstract = {Mobile malware poses a significant threat to user privacy and security, necessitating the development of effective detection techniques. In this paper, we propose a novel methodology for Android malware detection using a combination of feature selection based on Pufferfish Optimization (PFO) and Deep Convolutional Neural Network (DCNN) architecture. The proposed methodology involves collecting a diverse dataset of Android application binaries (APK files) from various sources and preprocessing the dataset by extracting relevant features such as permissions, API calls, and opcode sequences. Min-max normalization is then applied to scale the extracted features to a fixed range, followed by feature selection using the PFO algorithm to select the most discriminative features for malware detection. Subsequently, a DCNN architecture is designed to automatically learn hierarchical representations of the input data for effective malware detection. The model is trained, optimized, and evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate the effectiveness of the proposed methodology in accurately detecting Android malware, outperforming existing methods.},
        keywords = {malware detection, deep CNN, optimization, feature selection, standardization.},
        month = {July},
        }